class_docstr: Pipeline for unconditional image generation.
  
  This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods
  implemented for all pipelines (downloading, saving, running on a particular device, etc.).
  
  Parameters:
      unet ([`UNet2DModel`]):
          A `UNet2DModel` to denoise the encoded image latents.
      scheduler ([`PNDMScheduler`]):
          A `PNDMScheduler` to be used in combination with `unet` to denoise the encoded image.
code: def __init__(self, unet: UNet2DModel, scheduler: PNDMScheduler):
      super().__init__()
  
      scheduler = PNDMScheduler.from_config(scheduler.config)
  
      self.register_modules(unet=unet, scheduler=scheduler)
  
file_path: diffusers/src/diffusers/pipelines/deprecated/pndm/pipeline_pndm.py
imports: from typing import List, Optional, Tuple, Union
  import torch
  from ....models import UNet2DModel
  from ....schedulers import PNDMScheduler
  from ....utils.torch_utils import randn_tensor
  from ...pipeline_utils import DiffusionPipeline, ImagePipelineOutput
label_desc: Use image node_id 1 to create a new PNDMPipeline object from inherited base classes: DiffusionPipeline with example: obj = PNDMPipeline(unet, scheduler)